Classical artificial intelligence (ai) and probability based code infusion

ABSTRACT

A method, a computer system, and a computer program product for parallel conversion is provided. Embodiments of the present invention may include analyzing raw classical code using a code embedded deep learning model. Embodiments of the present invention may include analyzing running classical code using a deep learning model. Embodiments of the present invention may include marking a location of the raw classical code for a first quantum conversion. Embodiments of the present invention may include suggesting a memory size of the running classical code for a second quantum conversion. Embodiments of the present invention may include aggregating the raw classical code for the first quantum conversion. Embodiments of the present invention may include aggregating the running classical code for the second quantum conversion.

BACKGROUND

The present invention relates generally to the field of computing, andmore particularly to artificial intelligence (AI). Classical computingmay perform calculations and traditional numerical operations quickly. Ahigher volume of applications or computing intensive applications maycause performance issues on a classical computing system such that theamount of time for the programs to run may not be reasonable in aclassical computing environment.

SUMMARY

Embodiments of the present invention disclose a method, a computersystem, and a computer program product for parallel conversion.Embodiments of the present invention may include analyzing raw classicalcode using a code embedded deep learning model. Embodiments of thepresent invention may include analyzing running classical code using adeep learning model. Embodiments of the present invention may includemarking a location of the raw classical code for a first quantumconversion. Embodiments of the present invention may include suggestinga memory size of the running classical code for a second quantumconversion. Embodiments of the present invention may include aggregatingthe raw classical code for the first quantum conversion. Embodiments ofthe present invention may include aggregating the running classical codefor the second quantum conversion.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings. The various features of the drawings arenot to scale as the illustrations are for clarity in facilitating oneskilled in the art in understanding the invention in conjunction withthe detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2 is a block diagram of a parallel conversion categorization chartaccording to at least one embodiment;

FIG. 3 is an operational flowchart illustrating a parallel conversionassignment process according to at least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1, in accordance with anembodiment of the present disclosure; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5, in accordance with an embodiment of thepresent disclosure.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein, however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. This invention may, however, be embodied inmany different forms and should not be construed as limited to theexemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope of this invention to thoseskilled in the art. In the description, details of well-known featuresand techniques may be omitted to avoid unnecessarily obscuring thepresented embodiments.

As previously described, classical computing may perform calculationsand traditional numerical operations quickly. A higher volume ofapplications or computing intensive applications may cause performanceissues on a classical computing system such that the amount of time forthe programs to run may not be reasonable in a classical computingenvironment.

In a classical computing environment, the number of states that a binarycomputer with n bits can store is only one of 2{circumflex over ( )}nstates. The amount of information that may be stored in a quantumcomputer is much larger due to the quantum principles of superpositionand entanglement that allow information to be stored within differentstates. A quantum computer with n qubit(s) can store all of the2{circumflex over ( )}n bits. A qubit is a unit of quantum informationthat can be in a coherent superposition of more than one state or levelsimultaneously. A qubit may also be known as a quantum bit or a qbit.Quantum computing may act as an accelerator to computer processing andmay speed up bottlenecks that classical computers may encounter. Quantumcomputing may also solve problems that are not feasible or are notpractically feasible on a classical computer.

High computationally intensive applications may be difficult to run on aclassical computer within a reasonable amount of time. A classicalcomputational algorithm may be converted to quantum computing in orderto speed up the computational process. However, as more classicalcomputational algorithms are converted for use on a quantum computer,quantum computing machines may still have a limited capacity of qubits.Therefore, it may be advantageous to, among other things, provide asystem and method to determine which types of classical code or whichsections of classical code can be converted to quantum code.

The following described exemplary embodiments provide a system, methodand program product for parallel conversion. As such, embodiments of thepresent invention have the capacity to improve the technical field ofartificial intelligence by using machine learning to determine whichclassical code may be converted to quantum code. More specifically, deeplearning is used to identify parts or sections of classical code thatcan be converted to quantum code during runtime and during compile time.Quantum readiness is measured based on runtime tolerance for coherenceuncertainty. Additionally, a determination is made relating to whichsnippets or sections may be replaced by a quantum library and whichsnippets or sections do not use a quantum compiler.

Additional benefits may include discovering which classical code can beconverted into quantum code and storing the discoveries in a quantumlibrary. The quantum library may also be used to infuse quantum codeinto classical code. For example, deep measurements of code at runtimeand during compilation may be taken to determine if the code should beconverted either from classical code to quantum code, or vice versa.Deep measurements may include the extraction of descriptions about thecode and the application using deep learning models to the predictors ofcode at runtime. Determinations relating to whether the code should beconverted either from classical code to quantum code, or vice versa. Thedetermination may include factors such as a runtime signature (i.e.,input-output (TO), memory, or flops) or an algorithm signature todetermine is the code is a fit for quantum, or both factors may beanalyzed.

Additionally, user behavior may be used to influence quantum computinginfusion by determining which quantum computer algorithm may be neededor may be optimal. User behavior may include, for example, a user's riskassessment by measuring a user's risk level. The user's risk level maybe measured by, for example, capturing biometric signals from sensors ona smart phone or a smart watch that receives biometric data. A map thatrepresents a human's tolerance to risk to the operators of quantumcomputing may be created and used to determine if a piece of code hasquantum readiness. For example, if the number of shots required using ashot number determination to get a probabilistic answer is beyond thehuman's tolerance for risk, the piece is not ready for quantum based onthe user. Shots may include a form of sampling with quantum computingsuch that the higher number of shots may indicate that the observedprobable answer or prediction will likely result.

Various types of ML models may be built and used to create predictiveanalytics and results for the various industries. ML models may alsoinclude deep learning models and neural networks. Training and updatinga ML model may include supervised, unsupervised and semi-supervised MLprocedures. Supervised learning may use a labeled dataset or a labeledtraining set to build, train and update a model. Unsupervised learningmay use all unlabeled data to train a model. Semi-supervised learningmay use both labeled datasets and unlabeled datasets to train a model.

A neural network may be a component of deep learning. A neural networkmay be related to or known as a deep network or a deep neural network. Aneural network may interpret, label and classify raw data, such asunstructured data. A neuron in a deep neural network may combine inputdata and assign a weight to the input data based on a significance levelof what the neural network is learning in order to classify the data.The deeper the neural network, the more neurons or node layers the inputdata passes through. A neuron, a node and a filter may be consideredinterchangeable terms. The neuron may represent the location thatreceives input data, produces and associates an input weight to the dataand then determines, via a computation, if the data should continue orprogress further in the network before the data is classified. Eachlayer of neurons may train the data based on the previous output layer.

Deep learning is a type of machine learning that may classifyinformation based on the training data. The training data may bestructured data or unstructured data. Structured data may include datathat is highly organized, such as a spreadsheet, relational database ordata that is stored in a fixed field. Unstructured data may include datathat is not organized and has an unconventional internal structure, suchas a portable document format (PDF), an image, a presentation, awebpage, video content, audio content, an email, a word processingdocument or multimedia content. Deep learning may also be related to orknown as hierarchical learning or deep structured learning.

Deep learning may map an input, classify data, interpret datasets andprovide an output of data for one or more layers of data. Each layer ofdata may be represented as a node. A node may also be known as a neuronor an artificial neuron. Deep learning may detect similarities in datathat may or may not be labeled. For example, deep learning may operateas supervised learning, unsupervised learning or semi-supervisedlearning. Supervised learning may use a labeled dataset to train a MLmodel. Unsupervised learning may use all unlabeled data to train a MLmodel. Semi-supervised learning may use both labeled datasets andunlabeled datasets to train a ML model. The deep learning models mayprovide, for example, a graph output that may be generated as nodes andedges relating to the domain specific taxonomy that is being learned.

According to an embodiment, deep learning may be used to reduce thedimensionality of mapped features to the number of qubits on independentquantum chips given the number of algorithms to convert, the number ofavailable quantum chips, the digital parallel operations and the userinteractions of an application. Mapped features may include, forexample, weather predictors, sports statistics or independent variablesabout a data state. Larger encoding processes may be placed on largerqubit quantum machines, such as in the knapsack problem. The knapsackproblem is an optimization problem relating to resource allocation thatis aimed at maximizing a resource without overextending or wastingresources.

According to an embodiment, deep learning may be used to determine whichpart of classical code may be converted into quantum computingalgorithms. Input training data may include, for example, humanbiometric signals, shot number determination, lines of code, number ofpredictors or another feature that may describe a problem that aclassical algorithm is trying to solve. Deep leaning may be used toassist the computing system to determine if a particular section of thecode should or could use quantum computing. During training of themodel, the labeled data may include system performance (e.g., theprogram is running on a classical computing system) and a probabilitythat a solution is correct. A tradeoff may become apparent between asystem performance and a number of shots used to sample a quantumalgorithm for an answer.

An accuracy may be measured based on the system performance and aprobability that the answer is correct. An answer key may also be usedand accessed based on certain places or sections of code that a programshould or could be using quantum computing. If a deep learning algorithmprovides an incorrect answer, then the accuracy of the model may godown, and, correspondingly, if the deep learning algorithm provides acorrect answer, then the accuracy of the model may go up. Deep learningalgorithms used may, for example, include supervised machine learningthat were previously trained.

According to an embodiment, a software code scan may be performed, suchas a block format or a module format, using a statistical code analysisor a coverity tool, such as C, C++, Java or Javascript. The coveritytool may be used for coverage analysis to determine the number ofcomputations, assignable vectors and clock operations in a classicalcomputing environment that may be required to execute a certain block orcode file. Coverage analysis may analyze the complexity of the code. Thesoftware code scan may be performed over any software code or a sectionof software code.

In quantum computing, calculating the number of operations a quantumcomputer may perform per second may be described based on the number ofqubits. Although there currently may not be a standard answer tocalculating the number of operations, an estimation may be performed orcalculated based on a specific quantum chip. For example, an IBM Q® chip(IBM Q and all IBM Q-based trademarks and logos are trademarks orregistered trademarks of International Business Machines Corporationand/or its affiliates).

Using a gate specification of the particular quantum chip may providetimes in the forms of buffer times or computation times. These times maybe based on a physical operation of the qubit and the physicaloperations may be used to find quantum gate decompositions. Since thequantum operations may include physical operations that may not appearas timing, a buffer time may, for example, represent a pause betweenphysical operations. Calculating pauses between physical operationsbased on each gate may provide times for the numbers of operations, forexample, 3,000,000 operations per second.

According to an embodiment, a particular module or section of code maybe executed during run time, such as executing code that is currentlybeing processed or running code. Time and clock cycles may be internallycomputed to execute the running code and the running blocks of code witha number of computations inclusive of computationally intensiveoperations and memory space. The internally computed cycles may providea result that allows for an artificial exceeding (i.e., overclocking) orfor a limit of computing resources to emulate other devices as mobileplatforms. Operators or operands inputs may be correlated and stored inan embedded feature vector format of the program function. The embeddedfeature may describe or measure the behavior of the system's clocks thatmay also be used to input deep learning algorithms to determine if thesection of code could or should be converted to quantum code.

A one to one comparison may be computed with a determination of thenumber of qubits that may be required to perform a similar operation.For example, a 555 timer may be used with a clock oscillator working ina hardware component to fetch the difference in computation time andqubits may be computed to store the calculated computation time orfetched vector operations. A 555 timer is an integrated circuit (IC)that is used to time various components, such as timing pulsegenerations or timing oscillator applications.

A code block that already contains quantum code may be analyzed orevaluated to determine if classical computing code should or could besubstituted. Analyzing quantum code modules or quantum code enhancements(e.g., algorithms or libraries) using one or more deep learning networksmay consider a classification of groups. For example, considering twoclassification groups using deep learning may include one classificationgroup based on quantum computing and one classification based onclassical computing. The quantum computing classification may includeusing Grover's search and amplitude amplification to obtain anup-to-quadratic speed-ups. The quantum computing classification may alsoinclude an approach to encode relevant information into quantumamplitudes and to identify which quantum amplitudes have a potential forexponential improvements.

A time difference may be calculated between the difference in the timeexecution of blocks of code and the availability of qubits to performcomputationally intensive operations. The calculated difference mayprovide delta features between a digital operation and a quantumoperation. The calculated difference may also determine if the codeblock should or could be used as quantum code or classical code. Forexample, an evaluation of the time difference may be performed by usingauto-encoders or a principal component analysis (PCA) on the vectoredvariables and inputs. The evaluation may identify features that may bepruned, less relevant vectors, and features that may be retained, mostrelevant vectors, in order to maintain a similar or an improvedaccuracy.

For example, using a training dataset to label and classify the deeplearning dataset, the following code may be used:

-   -   def cancer{training_size, test_size, n, PLOT_DATA):    -   class_labels=[r′A′, r′B′]    -   data, target=datasets.load_cancer(True)    -   sample_train, sample_test, label_train, label_test=    -   train_test_split(data, target, test_size=0.3, random_state=12).        Standardizing the predictor for a gaussian distribution near        zero with a unit variance of 1 may be represented as:    -   std_scale=StandardScaler( ).fit(sample_train)    -   sample_train=std_scale.transform(sample_train)    -   sample_test=std_scale.transform(sample_test).        Reducing the number of features to a number of qubits may be        represented as:    -   pca=PCA(n_components=n).fit(sample_train)    -   sample_train=pca.transform(sample_train)    -   sample_test=pca.transform(sample_test).

A threshold of risk of 5% tolerance may be allocated to the vectors. Auser may set the threshold risk. If the fault tolerance threshold iscrossed, then the module will switch from quantum computing to using orperforming the execution using a classical approach or classicalcomputing.

A deep learning neural network (DLNN) model may be used to classifymultiple program fragments. For example, the pre-trained model mayprovide windows of code analysis when features are extracted from thecode and runtime performance. The extracted features may assist indetermining if the code should or could be executed as quantum code orclassical code. The deep learning neural network model may also be usedto categorize the computations and similar modules that can be processedusing a k-means clustering algorithm to determine if the code may beexecuted using classical computing or if the code may be executed bydeploying qubits and using quantum operations.

A graphical user interface (GUI) or a user interface (UI) may be usedfor filtering, delaying or changing the characteristics and highlightingthe nature of the code blocks that are to be executed by eitherclassical computing methods or quantum computing methods. The UI maydisplay markers or highlights placed around the code blocks based on thedistinction of which computing method will be used to process the blockof code that is marked or highlighted on the UI. A software analysis maythen be used to analyze the features and characteristics that were usedto determine the recommended action of processing the code block on aclassical computer or processing the code block using a quantumcomputer.

For example, a GUI may show a program or application with multipleblocks of code. Each block may, for example, be highlighted using twodifferent colors where one color of the highlighted code represents aclassical computing method and the second color highlighted coderepresents a quantum computing method. A physical representation of eachblock of code is presented such that quick and easy identification ofthe results of the deep learning model recommendation as to which partof the code should be processed by a classical computer and which partsof the code should be processed by a quantum computer.

According to an alternate embodiment, a photonic quantum circuit thatmimics a neural network may be used to classify the code as beingrequired to calculated using either classical computing methods orquantum computing methods. The photonic quantum circuit, for example,Xanadu (Xanadu and all Xanadu-based trademarks and logos are trademarksor registered trademarks of Xanadu Quantum Technologies, Inc. and/or itsaffiliates), contains interferometers and squeezing gates that may mimicthe weighing functions of a neural network, a displacement gate actingas a bias or a non-linear transformation similar to a rectified linearunit (ReLU) function of a neural network. A rectified linear unit (ReLU)function may include an activation function that is partially linear orthat will create an output for a positive input and a zero if the inputis not positive.

An evaluation may occur at the next time a new code is assessed. Theassessment may include a scanning of the code and a determination basedon which path to consider regarding classification. The same pieces orsections of code may continually be evaluated as the behavior of thesystem may be changing. For example, if the quantum computer is busy, aparallel conversion program will have to wait. The result of a busyquantum computer may boost the neural network classification of movingthe quantum code to classical computing.

A reinforcement model may be used to analyze a dynamic user's preferenceof switching the code blocks or the functions from quantum computing toclassical computing and the user's choice may be stored on a clouddatabased for future referencing.

Referring to FIG. 1, an exemplary networked computer environment 100 inaccordance with one embodiment is depicted. The networked computerenvironment 100 may include a computer 102 with a processor 104 and adata storage device 106 that is enabled to run a software program 108and a parallel conversion program 110 a. The networked computerenvironment 100 may also include a server 112 that is enabled to run aparallel conversion program 110 b that may interact with a database 114and a communication network 116. The networked computer environment 100may include a plurality of computers 102 and servers 112, only one ofwhich is shown. The communication network 116 may include various typesof communication networks, such as a wide area network (WAN), local areanetwork (LAN), a telecommunication network, a wireless network, a publicswitched network and/or a satellite network. It should be appreciatedthat FIG. 1 provides only an illustration of one implementation and doesnot imply any limitations with regard to the environments in whichdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

The client computer 102 may communicate with the server computer 112 viathe communications network 116. The communications network 116 mayinclude connections, such as wire, wireless communication links, orfiber optic cables. As will be discussed with reference to FIG. 4,server computer 112 may include internal components 902 a and externalcomponents 904 a, respectively, and client computer 102 may includeinternal components 902 b and external components 904 b, respectively.Server computer 112 may also operate in a cloud computing service model,such as Software as a Service (SaaS), Analytics as a Service (AaaS),Blockchain as a Service (BaaS), Platform as a Service (PaaS), orInfrastructure as a Service (IaaS). Server 112 may also be located in acloud computing deployment model, such as a private cloud, communitycloud, public cloud, or hybrid cloud. Client computer 102 may be, forexample, a mobile device, a telephone, a personal digital assistant, anetbook, a laptop computer, a tablet computer, a desktop computer, orany type of computing devices capable of running a program, accessing anetwork, and accessing a database 114. According to variousimplementations of the present embodiment, the parallel conversionprogram 110 a, 110 b may interact with a database 114 that may beembedded in various storage devices, such as, but not limited to acomputer/mobile device 102, a networked server 112, or a cloud storageservice.

According to the present embodiment, a user using a client computer 102or a server computer 112 may use the parallel conversion program 110 a,110 b (respectively) to determine which classical code may be convertedto quantum code. The parallel conversion method is explained in moredetail below with respect to FIGS. 2 and 3.

Referring now to FIG. 2, a block diagram of a parallel conversioncategorization chart 200 used by the parallel conversion program 110 a,110 b according to at least one embodiment is depicted. The parallelconversion chart may use machine learning to categorize whether a codeblock should be performed in a classical computing environment or aquantum computing environment. Different types of machine learning maybe used, such as deep learning, a feedforward neural network (FNN) or arecurrent neural network (RNN). The parallel conversions categorizationchart 200 may be used to identify optimal conditions as to whether thesystem or code block that is being processed or that will be processedshould use classical processing or quantum processing.

The choices for the type of algorithm in the parallel conversioncategorization chart 200 may include a classical algorithm or a quantumalgorithm. A classical algorithm may include an algorithm that may runon a classical computing environment and that provide a finite set ofsequence instructions performed on a classical computing device. Aquantum algorithm may include an algorithm that may also perform asequence of instructions, however, the quantum algorithm may beperformed on a quantum computer. For example, Shor's algorithm andGrover's algorithm, both algorithms performing computations much fasterthan a classical algorithm.

The choices for the type of data in the parallel conversioncategorization chart 200 may include classical data or quantum data.Classical data may include conventional data used to train a machinelearning, deep learning or neural network model. Quantum data mayinclude using the output data from a quantum computing device to train amachine learning, deep learning or neural network model.

A classical type of data with a classical type of algorithm (CC) mayrepresent classical data being processed on a classical computingenvironment. A CC category may include a classical machine learning.

A quantum type of data with a classical type of algorithm (QC) mayrepresent quantum data processed on a classical computing environment. AQC category may include using quantum variants of learning algorithms ina classical computing environment.

For a classical type of data with a quantum type of algorithm (CQ) mayrepresent classical data being processed on a quantum computing device.A CQ category may include using classical machine learning for quantumtasks or experiments in a quantum computing environment.

A quantum type of data with a quantum type of algorithm (QQ) mayrepresent quantum data being processed on a quantum computing device. AQQ category may include a quantum variant learning algorithm on aquantum computing device.

Referring now to FIG. 3, an operational flowchart illustrating theexemplary a parallel conversion assignment process 300 used by theparallel conversion program 110 a, 110 b according to at least oneembodiment is depicted. The parallel conversion assignment process mayuse machine learning for assigning a quantum computer to each parallelconversion.

At 302, model user behavior is received. User behavior may be collectedand measured using, for example, a smart watch or wearable computingdevices that collects biometric signals such as heartbeat or retinavariability and infrared or thermo grams. For example, if the heartbeatraises, then the number of shots increases. Infrared or thermo grams maybe analyzed by deep learning algorithms.

At 304, raw classical code is received. The raw classical code may bereceived by a service endpoint. The raw classical code may include codeor a section of code for analysis. The service endpoint may include aspecific service that the user is gaining access to, such as a websiteaddress or a uniform resource locator (URL).

At 306, the raw classical code is analyzed using deep learning with codeembedding. Code embedding or a code embedded deep learning model mayallow for large inputs and analysis and for reuse across multiple deeplearning models. For example, a pretrained deep learning analysis mayinclude a program application that provides fantasy footballpredictions, stock market analysis and medically diagnosed diseasemonitoring. The program application code is analyzed to identify whetheror not the classical code may be converted to quantum code.

At 308, a location for quantum conversion is marked. The location may bemarked, for example, using annotations or tokens. NLP may use tokens orannotations to mark locations by using, for example, tags or token tagsin the code.

At 310, code quantum conversions are aggregated. The code quantumconversions may be aggregated by, for example, a quantum applicationcrawler that searches code.

At 312, running classical code is received. The classical code may bereceived by, for example, a system monitoring application. The classicalcode may be running from an executing binary when received the systemmonitoring application.

At 314, the classical code is analyzed using deep learning. The deeplearning analysis may determine the viability of the classical code tobe used for or to be a good fit for a quantum code conversion. Forexample, the TO operations or the memory operations may be predictorsthat determine if the classical code should be transformed or changed toquantum code.

At 316, memory for quantum conversion is suggested. The suggestionrelating to memory size for a quantum conversion may include an outputcreated by the deep learning algorithm.

At 318, memory quantum conversations are aggregated. The aggregationrelating to the quantum conversion may include, for example, determininga memory use rate of change. The memory use rate of change may becalculated using a summation over the aggregated conversions. The systemmonitoring application may provide the net memory usage trended overtime.

At 320, a quantum computer is assigned to the parallel conversion. Thequantum computer is assigned to the parallel conversion by selecting aquantum algorithm from a library and replacing the classical computingcode with the quantum code. Additionally, a quantum computer may beassigned to the code block based on quantum resource availability.

It may be appreciated that FIGS. 2 and 3 provide only an illustration ofone embodiment and do not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted embodiment(s) may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 900 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment of the present invention. It should be appreciated that FIG.4 provides only an illustration of one implementation and does not implyany limitations with regard to the environments in which differentembodiments may be implemented. Many modifications to the depictedenvironments may be made based on design and implementationrequirements.

Data processing system 902, 904 is representative of any electronicdevice capable of executing machine-readable program instructions. Dataprocessing system 902, 904 may be representative of a smart phone, acomputer system, PDA, or other electronic devices. Examples of computingsystems, environments, and/or configurations that may represented bydata processing system 902, 904 include, but are not limited to,personal computer systems, server computer systems, thin clients, thickclients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, network PCs, minicomputer systems, anddistributed cloud computing environments that include any of the abovesystems or devices.

User client computer 102 and network server 112 may include respectivesets of internal components 902 a, b and external components 904 a, billustrated in FIG. 4. Each of the sets of internal components 902 a, bincludes one or more processors 906, one or more computer-readable RAMs908 and one or more computer-readable ROMs 910 on one or more buses 912,and one or more operating systems 914 and one or more computer-readabletangible storage devices 916. The one or more operating systems 914, thesoftware program 108, and the parallel conversion program 110 a inclient computer 102, and the parallel conversion program 110 b innetwork server 112, may be stored on one or more computer-readabletangible storage devices 916 for execution by one or more processors 906via one or more RAMs 908 (which typically include cache memory). In theembodiment illustrated in FIG. 4, each of the computer-readable tangiblestorage devices 916 is a magnetic disk storage device of an internalhard drive. Alternatively, each of the computer-readable tangiblestorage devices 916 is a semiconductor storage device such as ROM 910,EPROM, flash memory or any other computer-readable tangible storagedevice that can store a computer program and digital information.

Each set of internal components 902 a, b also includes a R/W drive orinterface 918 to read from and write to one or more portablecomputer-readable tangible storage devices 920 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 and the parallel conversion program 110 a, 110 b can bestored on one or more of the respective portable computer-readabletangible storage devices 920, read via the respective R/W drive orinterface 918 and loaded into the respective hard drive 916.

Each set of internal components 902 a, b may also include networkadapters (or switch port cards) or interfaces 922 such as a TCP/IPadapter cards, wireless wi-fi interface cards, or 3G or 4G wirelessinterface cards or other wired or wireless communication links. Thesoftware program 108 and the parallel conversion program 110 a in clientcomputer 102 and the parallel conversion program 110 b in network servercomputer 112 can be downloaded from an external computer (e.g., server)via a network (for example, the Internet, a local area network or other,wide area network) and respective network adapters or interfaces 922.From the network adapters (or switch port adaptors) or interfaces 922,the software program 108 and the parallel conversion program 110 a inclient computer 102 and the parallel conversion program 110 b in networkserver computer 112 are loaded into the respective hard drive 916. Thenetwork may comprise copper wires, optical fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers.

Each of the sets of external components 904 a, b can include a computerdisplay monitor 924, a keyboard 926, and a computer mouse 928. Externalcomponents 904 a, b can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 902 a, b also includes device drivers930 to interface to computer display monitor 924, keyboard 926 andcomputer mouse 928. The device drivers 930, R/W drive or interface 918and network adapter or interface 922 comprise hardware and software(stored in storage device 916 and/or ROM 910).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure oron a hybrid cloud infrastructure. The applications are accessible fromvarious client devices through a thin client interface such as a webbrowser (e.g., web-based e-mail). The consumer does not manage orcontrol the underlying cloud infrastructure including network, servers,operating systems, storage, or even individual application capabilities,with the possible exception of limited user-specific applicationconfiguration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Analytics as a Service (AaaS): the capability provided to the consumeris to use web-based or cloud-based networks (i.e., infrastructure) toaccess an analytics platform. Analytics platforms may include access toanalytics software resources or may include access to relevantdatabases, corpora, servers, operating systems or storage. The consumerdoes not manage or control the underlying web-based or cloud-basedinfrastructure including databases, corpora, servers, operating systemsor storage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 5, illustrative cloud computing environment 1000is depicted. As shown, cloud computing environment 1000 comprises one ormore cloud computing nodes 100 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1000A, desktop computer 1000B, laptopcomputer 1000C, and/or automobile computer system 1000N may communicate.Nodes 100 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 1000to offer infrastructure, platforms and/or software as services for whicha cloud consumer does not need to maintain resources on a localcomputing device. It is understood that the types of computing devices1000A-N shown in FIG. 5 are intended to be illustrative only and thatcomputing nodes 100 and cloud computing environment 1000 can communicatewith any type of computerized device over any type of network and/ornetwork addressable connection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers 1100provided by cloud computing environment 1000 is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 1102 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1104;RISC (Reduced Instruction Set Computer) architecture based servers 1106;servers 1108; blade servers 1110; storage devices 1112; and networks andnetworking components 1114. In some embodiments, software componentsinclude network application server software 1116 and database software1118.

Virtualization layer 1120 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1122; virtual storage 1124; virtual networks 1126, including virtualprivate networks; virtual applications and operating systems 1128; andvirtual clients 1130.

In one example, management layer 1132 may provide the functionsdescribed below. Resource provisioning 1134 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1136provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1138 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1140provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1142 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1144 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 1146; software development and lifecycle management 1148;virtual classroom education delivery 1150; data analytics processing1152; transaction processing 1154; and parallel conversion 1156. Aparallel conversion program 110 a, 110 b provides a way to determinewhich classical code may be converted to quantum code.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language, python programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for parallel conversion, the methodcomprising: analyzing raw classical code; analyzing running classicalcode; marking a location of the raw classical code for a first quantumconversion; suggesting a memory size of the running classical code for asecond quantum conversion; aggregating the raw classical code for thefirst quantum conversion; and aggregating the running classical code forthe second quantum conversion.
 2. The method of claim 1, furthercomprising: receiving a user behavior measurement; receiving the rawclassical code; receiving the running classical code; and assigning aquantum computer to the parallel conversion based on the received userbehavior measurement, the aggregated raw classical code and theaggregated running classical code.
 3. The method of claim 1, wherein theraw classical code is analyzed using a deep learning model with a codeembedding.
 4. The method of claim 1, wherein the running classical codeis analyzed using a deep learning model.
 5. The method of claim 1,wherein the location of the raw classical code is marked using naturallanguage processing annotations.
 6. The method of claim 2, wherein theuser behavior is measured using biometric data received from the uservia a sensor.
 7. The method of claim 2, wherein the quantum computer isassigned to a code block based on a quantum resource availability.
 8. Acomputer system for parallel conversion, comprising: one or moreprocessors, one or more computer-readable memories, one or morecomputer-readable tangible storage media, and program instructionsstored on at least one of the one or more computer-readable tangiblestorage media for execution by at least one of the one or moreprocessors via at least one of the one or more computer-readablememories, wherein the computer system is capable of performing a methodcomprising: analyzing raw classical code; analyzing running classicalcode; marking a location of the raw classical code for a first quantumconversion; suggesting a memory size of the running classical code for asecond quantum conversion; aggregating the raw classical code for thefirst quantum conversion; and aggregating the running classical code forthe second quantum conversion.
 9. The computer system of claim 8,further comprising: receiving a user behavior measurement; receiving theraw classical code; receiving the running classical code; and assigninga quantum computer to the parallel conversion based on the received userbehavior measurement, the aggregated raw classical code and theaggregated running classical code.
 10. The computer system of claim 8,wherein the raw classical code is analyzed using a deep learning modelwith a code embedding.
 11. The computer system of claim 8, wherein therunning classical code is analyzed using a deep learning model.
 12. Thecomputer system of claim 8, wherein the location of the raw classicalcode is marked using natural language processing annotations.
 13. Thecomputer system of claim 9, wherein the user behavior is measured usingbiometric data received from the user via a sensor.
 14. The computersystem of claim 9, wherein the quantum computer is assigned to a codeblock based on a quantum resource availability.
 15. A computer programproduct for parallel conversion, comprising: one or morecomputer-readable tangible storage media and program instructions storedon at least one of the one or more computer-readable tangible storagemedia, the program instructions executable by a processor to cause theprocessor to perform a method comprising: analyzing raw classical code;analyzing running classical code; marking a location of the rawclassical code for a first quantum conversion; suggesting a memory sizeof the running classical code for a second quantum conversion;aggregating the raw classical code for the first quantum conversion; andaggregating the running classical code for the second quantumconversion.
 16. The computer program product of claim 15, furthercomprising: receiving a user behavior measurement; receiving the rawclassical code; receiving the running classical code; and assigning aquantum computer to the parallel conversion based on the received userbehavior measurement, the aggregated raw classical code and theaggregated running classical code.
 17. The computer program product ofclaim 15, wherein the raw classical code is analyzed using a deeplearning model with a code embedding.
 18. The computer program productof claim 15, wherein the running classical code is analyzed using a deeplearning model.
 19. The computer program product of claim 15, whereinthe location of the raw classical code is marked using natural languageprocessing annotations.
 20. The computer program product of claim 16,wherein the user behavior is measured using biometric data received fromthe user via a sensor.